Dean Lee

Whether or not femto-scale droplets of quark-gluon plasma (QGP) are formed in so-called small systems at high-energy colliders is a pressing question in the phenomenology of the strong interaction. For proton-proton or proton-nucleus collisions the answer is inconclusive due to the large theoretical uncertainties plaguing the description of these processes. While upcoming data on collisions of O 16 nuclei may mitigate these uncertainties in the near future, here we demonstrate the unique possibilities offered by complementing O 16 + O 16 data with collisions of Ne 20 ions. We couple both nuclear lattice effective field theory (NLEFT) and projected generator coordinate method (PGCM) descriptions of the structure of Ne 20 and O 16 to hydrodynamic simulations of O 16 + O 16 and Ne 20 + Ne 20 collisions at high energy. We isolate the imprints of the bowling-pin shape of Ne 20 on the collective flow of hadrons, which can be used to perform quantitative tests of the hydrodynamic QGP paradigm. In particular, we predict that the elliptic flow of Ne 20 + Ne 20 collisions is enhanced by as much as 1.174 ( 8 ) stat ( 31 ) syst for NLEFT and 1.139 ( 6 ) stat ( 39 ) syst for PGCM relative to O 16 + O 16 collisions for the 1% most central events. At the same time, theoretical uncertainties largely cancel when studying relative variations of observables between two systems. This demonstrates a method based on experiments with two light-ion species for precision characterizations of the collective dynamics and its emergence in a small system.

We present a general class of machine learning algorithms called parametric matrix models. In contrast with most existing machine learning models that imitate the biology of neurons, parametric matrix models use matrix equations that emulate physical systems. Similar to how physics problems are usually solved, parametric matrix models learn the governing equations that lead to the desired outputs. Parametric matrix models can be efficiently trained from empirical data, and the equations may use algebraic, differential, or integral relations. While originally designed for scientific computing, we prove that parametric matrix models are universal function approximators that can be applied to general machine learning problems. After introducing the underlying theory, we apply parametric matrix models to a series of different challenges that show their performance for a wide range of problems. For all the challenges tested here, parametric matrix models produce accurate results within an efficient and interpretable computational framework that allows for input feature extrapolation.

This white paper is the result of a collaboration by many of those that attended a workshop at the facility for rare isotope beams (FRIB), organized by the FRIB Theory Alliance (FRIB-TA), on ‘Theoretical Justifications and Motivations for Early High-Profile FRIB Experiments’. It covers a wide range of topics related to the science that will be explored at FRIB. After a brief introduction, the sections address: section 2: Overview of theoretical methods, section 3: Experimental capabilities, section 4: Structure, section 5: Near-threshold Physics, section 6: Reaction mechanisms, section 7: Nuclear equations of state, section 8: Nuclear astrophysics, section 9: Fundamental symmetries, and section 10: Experimental design and uncertainty quantification.

We present a systematic study of the low-lying states in beryllium isotopes from Be 7 to Be 12 using nuclear lattice effective field theory with the N 3 LO interaction. Our calculations achieve good agreement with experimental data for energies, radii, and electromagnetic properties. We introduce a novel, model-independent method to quantify nuclear shapes, uncovering a distinct pattern in the interplay between positive and negative parity states across the isotopic chain. By combining Monte Carlo sampling of the many-body density operator with a novel nucleon-grouping algorithm, the prominent two-center cluster structures, the emergence of one-neutron halo, complex nuclear molecular dynamics such as π orbital and σ orbital, emerge naturally.

We present a comparison of different quantum state preparation algorithms and their overall efficiency for the Schwinger model with a theta term. While adiabatic state preparation is proved to be effective, in practice it leads to large gate counts to prepare the ground state. The quantum approximate optimization algorithm (QAOA) provides excellent results while keeping the counts small by design, at the cost of an expensive classical minimization process. We introduce a “blocked” modification of the Schwinger Hamiltonian to be used in the QAOA that further decreases the length of the algorithms as the size of the problem is increased. The rodeo algorithm (RA) provides a powerful tool to efficiently prepare any eigenstate of the Hamiltonian, as long as its overlap with the initial guess is large enough. We obtain the best results when combining the blocked QAOA ansatz and the RA, as this provides an excellent initial state with a relatively short algorithm without the need to perform any classical steps for large problem sizes.